Overview

Dataset statistics

Number of variables72
Number of observations45718
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.1 MiB
Average record size in memory576.0 B

Variable types

Numeric27
Categorical45

Alerts

AIIND102 is highly overall correlated with COUTYP2 and 44 other fieldsHigh correlation
ANYHLTI2 is highly overall correlated with HLCLAST and 12 other fieldsHigh correlation
CAIDCHIP is highly overall correlated with GRPHLTIN and 2 other fieldsHigh correlation
CHAMPUS is highly overall correlated with IICHMPUSHigh correlation
COUTYP2 is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
GOVTPROG is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
GRPHLTIN is highly overall correlated with CAIDCHIP and 2 other fieldsHigh correlation
HLCALL99 is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
HLCALLFG is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
HLCLAST is highly overall correlated with ANYHLTI2 and 6 other fieldsHigh correlation
HLCNOTMO is highly overall correlated with HLCNOTYRHigh correlation
HLCNOTYR is highly overall correlated with ANYHLTI2 and 7 other fieldsHigh correlation
HLLOSRSN is highly overall correlated with ANYHLTI2 and 5 other fieldsHigh correlation
HLNVCOST is highly overall correlated with ANYHLTI2 and 4 other fieldsHigh correlation
HLNVNEED is highly overall correlated with ANYHLTI2 and 4 other fieldsHigh correlation
HLNVOFFR is highly overall correlated with ANYHLTI2 and 4 other fieldsHigh correlation
HLNVREF is highly overall correlated with ANYHLTI2 and 4 other fieldsHigh correlation
HLNVSOR is highly overall correlated with ANYHLTI2 and 4 other fieldsHigh correlation
HLTINNOS is highly overall correlated with ANYHLTI2 and 6 other fieldsHigh correlation
IFATHER is highly overall correlated with AIIND102 and 45 other fieldsHigh correlation
IICHMPUS is highly overall correlated with AIIND102 and 45 other fieldsHigh correlation
IIFAMIN3 is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IIFAMPMT is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IIFAMSOC is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IIFAMSSI is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IIFAMSVC is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IIFSTAMP is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IIHH65_2 is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IIHHSIZ2 is highly overall correlated with AIIND102 and 45 other fieldsHigh correlation
IIINSUR4 is highly overall correlated with AIIND102 and 45 other fieldsHigh correlation
IIKI17_2 is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IIMCDCHP is highly overall correlated with AIIND102 and 46 other fieldsHigh correlation
IIMEDICR is highly overall correlated with AIIND102 and 45 other fieldsHigh correlation
IIOTHHLT is highly overall correlated with AIIND102 and 48 other fieldsHigh correlation
IIPINC3 is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IIPRVHLT is highly overall correlated with AIIND102 and 46 other fieldsHigh correlation
IIWELMOS is highly overall correlated with AIIND102 and 45 other fieldsHigh correlation
IRCHMPUS is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IRFAMIN3 is highly overall correlated with AIIND102 and 42 other fieldsHigh correlation
IRFAMPMT is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IRFAMSOC is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IRFAMSSI is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IRFAMSVC is highly overall correlated with AIIND102 and 45 other fieldsHigh correlation
IRFSTAMP is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IRHH65_2 is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IRHHSIZ2 is highly overall correlated with AIIND102 and 41 other fieldsHigh correlation
IRINSUR4 is highly overall correlated with AIIND102 and 48 other fieldsHigh correlation
IRKI17_2 is highly overall correlated with AIIND102 and 45 other fieldsHigh correlation
IRMCDCHP is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IRMEDICR is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
IROTHHLT is highly overall correlated with AIIND102 and 48 other fieldsHigh correlation
IRPINC3 is highly overall correlated with AIIND102 and 41 other fieldsHigh correlation
IRPRVHLT is highly overall correlated with AIIND102 and 45 other fieldsHigh correlation
IRWELMOS is highly overall correlated with IIWELMOS and 1 other fieldsHigh correlation
MAIIN102 is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
MEDICARE is highly overall correlated with IIMEDICRHigh correlation
NRCH17_2 is highly overall correlated with IIHHSIZ2 and 1 other fieldsHigh correlation
OTHINS is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
PDEN10 is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
POVERTY3 is highly overall correlated with IRFAMIN3High correlation
PRVHLTIN is highly overall correlated with CAIDCHIP and 2 other fieldsHigh correlation
PRXYDATA is highly overall correlated with IFATHERHigh correlation
TOOLONG is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
TROUBUND is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
VEREP is highly overall correlated with AIIND102 and 44 other fieldsHigh correlation
VESTR is highly overall correlated with AIIND102 and 41 other fieldsHigh correlation
IFATHER is highly imbalanced (57.0%)Imbalance
IIHHSIZ2 is highly imbalanced (99.3%)Imbalance
IIKI17_2 is highly imbalanced (97.7%)Imbalance
IRHH65_2 is highly imbalanced (67.7%)Imbalance
IIHH65_2 is highly imbalanced (97.2%)Imbalance
IRMCDCHP is highly imbalanced (51.2%)Imbalance
IIMCDCHP is highly imbalanced (95.0%)Imbalance
IRMEDICR is highly imbalanced (73.9%)Imbalance
IIMEDICR is highly imbalanced (97.1%)Imbalance
IRCHMPUS is highly imbalanced (85.2%)Imbalance
IICHMPUS is highly imbalanced (98.2%)Imbalance
IIPRVHLT is highly imbalanced (96.1%)Imbalance
IROTHHLT is highly imbalanced (65.9%)Imbalance
IIOTHHLT is highly imbalanced (68.0%)Imbalance
HLCALLFG is highly imbalanced (99.8%)Imbalance
HLCALL99 is highly imbalanced (99.8%)Imbalance
IRINSUR4 is highly imbalanced (69.6%)Imbalance
IIINSUR4 is highly imbalanced (95.2%)Imbalance
OTHINS is highly imbalanced (62.3%)Imbalance
IRFAMSOC is highly imbalanced (59.6%)Imbalance
IIFAMSOC is highly imbalanced (95.1%)Imbalance
IRFAMSSI is highly imbalanced (77.3%)Imbalance
IIFAMSSI is highly imbalanced (95.1%)Imbalance
IRFSTAMP is highly imbalanced (54.4%)Imbalance
IIFSTAMP is highly imbalanced (97.1%)Imbalance
IRFAMPMT is highly imbalanced (88.8%)Imbalance
IIFAMPMT is highly imbalanced (96.0%)Imbalance
IRFAMSVC is highly imbalanced (85.8%)Imbalance
IIFAMSVC is highly imbalanced (96.8%)Imbalance
IIWELMOS is highly imbalanced (81.2%)Imbalance
IIPINC3 is highly imbalanced (87.5%)Imbalance
IIFAMIN3 is highly imbalanced (71.0%)Imbalance
GOVTPROG is highly imbalanced (50.1%)Imbalance
TOOLONG is highly imbalanced (79.8%)Imbalance
TROUBUND is highly imbalanced (82.8%)Imbalance
MAIIN102 is highly imbalanced (90.7%)Imbalance
AIIND102 is highly imbalanced (90.6%)Imbalance
Criminal is highly imbalanced (63.6%)Imbalance
CELLNOTCL is highly skewed (γ1 = 22.78471443)Skewed
CELLWRKNG is highly skewed (γ1 = 27.76120671)Skewed
VESTR is highly skewed (γ1 = -150.4973134)Skewed
PERID has unique valuesUnique

Reproduction

Analysis started2026-02-16 00:24:18.371560
Analysis finished2026-02-16 00:26:07.231504
Duration1 minute and 48.86 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

PERID
Real number (ℝ)

Unique 

Distinct45718
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54454464
Minimum10002216
Maximum99999555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.3 KiB
2026-02-16T05:56:07.433175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10002216
5-th percentile14646483
Q132331888
median54110427
Q376127308
95-th percentile94806844
Maximum99999555
Range89997339
Interquartile range (IQR)43795420

Descriptive statistics

Standard deviation25539108
Coefficient of variation (CV)0.46899934
Kurtosis-1.1753423
Mean54454464
Median Absolute Deviation (MAD)21906778
Skewness0.0080238984
Sum2.4895492 × 1012
Variance6.5224602 × 1014
MonotonicityNot monotonic
2026-02-16T05:56:07.569920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250951431
 
< 0.1%
130051431
 
< 0.1%
674151431
 
< 0.1%
709251431
 
< 0.1%
752351431
 
< 0.1%
477451431
 
< 0.1%
331451431
 
< 0.1%
637651431
 
< 0.1%
577961431
 
< 0.1%
664161431
 
< 0.1%
Other values (45708)45708
> 99.9%
ValueCountFrequency (%)
100022161
< 0.1%
100029471
< 0.1%
100040881
< 0.1%
100044371
< 0.1%
100102451
< 0.1%
100118381
< 0.1%
100143851
< 0.1%
100188351
< 0.1%
100203431
< 0.1%
100238361
< 0.1%
ValueCountFrequency (%)
999995551
< 0.1%
999962781
< 0.1%
999949481
< 0.1%
999945241
< 0.1%
999922041
< 0.1%
999892771
< 0.1%
999887581
< 0.1%
999819931
< 0.1%
999793441
< 0.1%
999782691
< 0.1%

IFATHER
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
4
34873 
1
7780 
2
 
3050
3
 
13
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
434873
76.3%
17780
 
17.0%
23050
 
6.7%
313
 
< 0.1%
-12
 
< 0.1%

Length

2026-02-16T05:56:07.741482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:07.863699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
434873
76.3%
17782
 
17.0%
23050
 
6.7%
313
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
434873
76.3%
17782
 
17.0%
23050
 
6.7%
313
 
< 0.1%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
434873
76.3%
17782
 
17.0%
23050
 
6.7%
313
 
< 0.1%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
434873
76.3%
17782
 
17.0%
23050
 
6.7%
313
 
< 0.1%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
434873
76.3%
17782
 
17.0%
23050
 
6.7%
313
 
< 0.1%
-2
 
< 0.1%

NRCH17_2
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
0
33475 
1
4999 
2
4611 
3
 
2549
-1
 
84

Length

Max length2
Median length1
Mean length1.0018374
Min length1

Characters and Unicode

Total characters45802
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
033475
73.2%
14999
 
10.9%
24611
 
10.1%
32549
 
5.6%
-184
 
0.2%

Length

2026-02-16T05:56:08.036234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:08.103822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
033475
73.2%
15083
 
11.1%
24611
 
10.1%
32549
 
5.6%

Most occurring characters

ValueCountFrequency (%)
033475
73.1%
15083
 
11.1%
24611
 
10.1%
32549
 
5.6%
-84
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)45802
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
033475
73.1%
15083
 
11.1%
24611
 
10.1%
32549
 
5.6%
-84
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45802
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
033475
73.1%
15083
 
11.1%
24611
 
10.1%
32549
 
5.6%
-84
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45802
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
033475
73.1%
15083
 
11.1%
24611
 
10.1%
32549
 
5.6%
-84
 
0.2%

IRHHSIZ2
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4263747
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:08.171615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum6
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4274201
Coefficient of variation (CV)0.41659779
Kurtosis-0.84975603
Mean3.4263747
Median Absolute Deviation (MAD)1
Skewness0.16281285
Sum156647
Variance2.0375282
MonotonicityNot monotonic
2026-02-16T05:56:08.274507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
410933
23.9%
210258
22.4%
310173
22.3%
56175
13.5%
64566
10.0%
13611
 
7.9%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
13611
 
7.9%
210258
22.4%
310173
22.3%
410933
23.9%
56175
13.5%
64566
10.0%
ValueCountFrequency (%)
64566
10.0%
56175
13.5%
410933
23.9%
310173
22.3%
210258
22.4%
13611
 
7.9%
-12
 
< 0.1%

IIHHSIZ2
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
45675 
3
 
41
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145675
99.9%
341
 
0.1%
-12
 
< 0.1%

Length

2026-02-16T05:56:08.382106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:08.451411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
145677
99.9%
341
 
0.1%

Most occurring characters

ValueCountFrequency (%)
145677
99.9%
341
 
0.1%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
145677
99.9%
341
 
0.1%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
145677
99.9%
341
 
0.1%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
145677
99.9%
341
 
0.1%
-2
 
< 0.1%

IRKI17_2
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
19258 
2
10126 
3
9554 
4
6778 
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row1
5th row4

Common Values

ValueCountFrequency (%)
119258
42.1%
210126
22.1%
39554
20.9%
46778
 
14.8%
-12
 
< 0.1%

Length

2026-02-16T05:56:08.564918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:08.713530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
119260
42.1%
210126
22.1%
39554
20.9%
46778
 
14.8%

Most occurring characters

ValueCountFrequency (%)
119260
42.1%
210126
22.1%
39554
20.9%
46778
 
14.8%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
119260
42.1%
210126
22.1%
39554
20.9%
46778
 
14.8%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
119260
42.1%
210126
22.1%
39554
20.9%
46778
 
14.8%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
119260
42.1%
210126
22.1%
39554
20.9%
46778
 
14.8%
-2
 
< 0.1%

IIKI17_2
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
45544 
3
 
172
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145544
99.6%
3172
 
0.4%
-12
 
< 0.1%

Length

2026-02-16T05:56:08.802615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:08.890891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
145546
99.6%
3172
 
0.4%

Most occurring characters

ValueCountFrequency (%)
145546
99.6%
3172
 
0.4%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
145546
99.6%
3172
 
0.4%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
145546
99.6%
3172
 
0.4%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
145546
99.6%
3172
 
0.4%
-2
 
< 0.1%

IRHH65_2
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
40188 
2
 
3618
3
 
1910
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
140188
87.9%
23618
 
7.9%
31910
 
4.2%
-12
 
< 0.1%

Length

2026-02-16T05:56:08.970346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:09.070844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
140190
87.9%
23618
 
7.9%
31910
 
4.2%

Most occurring characters

ValueCountFrequency (%)
140190
87.9%
23618
 
7.9%
31910
 
4.2%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
140190
87.9%
23618
 
7.9%
31910
 
4.2%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
140190
87.9%
23618
 
7.9%
31910
 
4.2%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
140190
87.9%
23618
 
7.9%
31910
 
4.2%
-2
 
< 0.1%

IIHH65_2
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
45443 
3
 
235
2
 
38
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145443
99.4%
3235
 
0.5%
238
 
0.1%
-12
 
< 0.1%

Length

2026-02-16T05:56:09.167891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:09.265900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
145445
99.4%
3235
 
0.5%
238
 
0.1%

Most occurring characters

ValueCountFrequency (%)
145445
99.4%
3235
 
0.5%
238
 
0.1%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
145445
99.4%
3235
 
0.5%
238
 
0.1%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
145445
99.4%
3235
 
0.5%
238
 
0.1%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
145445
99.4%
3235
 
0.5%
238
 
0.1%
-2
 
< 0.1%

PRXRETRY
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.394943
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:09.357798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile99
Q199
median99
Q399
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.355156
Coefficient of variation (CV)0.12685624
Kurtosis55.642376
Mean97.394943
Median Absolute Deviation (MAD)0
Skewness-7.5916092
Sum4452702
Variance152.64989
MonotonicityNot monotonic
2026-02-16T05:56:09.427589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
9944865
98.1%
2752
 
1.6%
9861
 
0.1%
9433
 
0.1%
975
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
2752
 
1.6%
9433
 
0.1%
975
 
< 0.1%
9861
 
0.1%
9944865
98.1%
ValueCountFrequency (%)
9944865
98.1%
9861
 
0.1%
975
 
< 0.1%
9433
 
0.1%
2752
 
1.6%
-12
 
< 0.1%

PRXYDATA
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.874098
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:09.528629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median99
Q399
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)98

Descriptive statistics

Standard deviation44.325675
Coefficient of variation (CV)0.62541431
Kurtosis-1.112696
Mean70.874098
Median Absolute Deviation (MAD)0
Skewness-0.9419692
Sum3240222
Variance1964.7655
MonotonicityNot monotonic
2026-02-16T05:56:09.657573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9932502
71.1%
113079
28.6%
9861
 
0.1%
238
 
0.1%
9433
 
0.1%
973
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
113079
28.6%
238
 
0.1%
9433
 
0.1%
973
 
< 0.1%
9861
 
0.1%
9932502
71.1%
ValueCountFrequency (%)
9932502
71.1%
9861
 
0.1%
973
 
< 0.1%
9433
 
0.1%
238
 
0.1%
113079
28.6%
-12
 
< 0.1%

MEDICARE
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3714511
Minimum-1
Maximum98
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:09.783179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum98
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.4899215
Coefficient of variation (CV)2.7366879
Kurtosis198.76134
Mean2.3714511
Median Absolute Deviation (MAD)0
Skewness14.152206
Sum108418
Variance42.119081
MonotonicityNot monotonic
2026-02-16T05:56:09.927257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
241703
91.2%
13789
 
8.3%
94159
 
0.3%
9739
 
0.1%
9822
 
< 0.1%
854
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
13789
 
8.3%
241703
91.2%
854
 
< 0.1%
94159
 
0.3%
9739
 
0.1%
9822
 
< 0.1%
ValueCountFrequency (%)
9822
 
< 0.1%
9739
 
0.1%
94159
 
0.3%
854
 
< 0.1%
241703
91.2%
13789
 
8.3%
-12
 
< 0.1%

CAIDCHIP
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6520845
Minimum-1
Maximum98
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:10.043854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median2
Q32
95-th percentile2
Maximum98
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.9482867
Coefficient of variation (CV)3.3740579
Kurtosis99.464657
Mean2.6520845
Median Absolute Deviation (MAD)0
Skewness10.055259
Sum121248
Variance80.071834
MonotonicityNot monotonic
2026-02-16T05:56:10.119749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
235030
76.6%
110248
 
22.4%
94318
 
0.7%
8551
 
0.1%
9747
 
0.1%
9822
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
110248
 
22.4%
235030
76.6%
8551
 
0.1%
94318
 
0.7%
9747
 
0.1%
9822
 
< 0.1%
ValueCountFrequency (%)
9822
 
< 0.1%
9747
 
0.1%
94318
 
0.7%
8551
 
0.1%
235030
76.6%
110248
 
22.4%
-12
 
< 0.1%

CHAMPUS
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2244849
Minimum-1
Maximum98
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:10.212522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q12
median2
Q32
95-th percentile2
Maximum98
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.9447836
Coefficient of variation (CV)2.2228893
Kurtosis349.3296
Mean2.2244849
Median Absolute Deviation (MAD)0
Skewness18.7227
Sum101699
Variance24.450885
MonotonicityNot monotonic
2026-02-16T05:56:10.291997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
243849
95.9%
11738
 
3.8%
9474
 
0.2%
9729
 
0.1%
9822
 
< 0.1%
854
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
11738
 
3.8%
243849
95.9%
854
 
< 0.1%
9474
 
0.2%
9729
 
0.1%
9822
 
< 0.1%
ValueCountFrequency (%)
9822
 
< 0.1%
9729
 
0.1%
9474
 
0.2%
854
 
< 0.1%
243849
95.9%
11738
 
3.8%
-12
 
< 0.1%

PRVHLTIN
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0431996
Minimum-1
Maximum98
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:10.370879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum98
Range99
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.8093516
Coefficient of variation (CV)3.8221188
Kurtosis136.11438
Mean2.0431996
Median Absolute Deviation (MAD)0
Skewness11.726661
Sum93411
Variance60.985972
MonotonicityNot monotonic
2026-02-16T05:56:10.490284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
127827
60.9%
217567
38.4%
94252
 
0.6%
9744
 
0.1%
9822
 
< 0.1%
854
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
127827
60.9%
217567
38.4%
854
 
< 0.1%
94252
 
0.6%
9744
 
0.1%
9822
 
< 0.1%
ValueCountFrequency (%)
9822
 
< 0.1%
9744
 
0.1%
94252
 
0.6%
854
 
< 0.1%
217567
38.4%
127827
60.9%
-12
 
< 0.1%

GRPHLTIN
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.55208
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:10.604999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q399
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)98

Descriptive statistics

Standard deviation47.786379
Coefficient of variation (CV)1.2081888
Kurtosis-1.8072951
Mean39.55208
Median Absolute Deviation (MAD)0
Skewness0.43883274
Sum1808242
Variance2283.538
MonotonicityNot monotonic
2026-02-16T05:56:10.730575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
124551
53.7%
9917567
38.4%
23205
 
7.0%
98275
 
0.6%
9470
 
0.2%
9745
 
0.1%
853
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
124551
53.7%
23205
 
7.0%
853
 
< 0.1%
9470
 
0.2%
9745
 
0.1%
98275
 
0.6%
9917567
38.4%
ValueCountFrequency (%)
9917567
38.4%
98275
 
0.6%
9745
 
0.1%
9470
 
0.2%
853
 
< 0.1%
23205
 
7.0%
124551
53.7%
-12
 
< 0.1%

HLTINNOS
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.073953
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:10.838198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile2
Q199
median99
Q399
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation33.00842
Coefficient of variation (CV)0.38348906
Kurtosis2.6785495
Mean86.073953
Median Absolute Deviation (MAD)0
Skewness-2.1629015
Sum3935129
Variance1089.5558
MonotonicityNot monotonic
2026-02-16T05:56:10.896812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9939592
86.6%
24643
 
10.2%
11431
 
3.1%
9422
 
< 0.1%
9822
 
< 0.1%
976
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
11431
 
3.1%
24643
 
10.2%
9422
 
< 0.1%
976
 
< 0.1%
9822
 
< 0.1%
9939592
86.6%
ValueCountFrequency (%)
9939592
86.6%
9822
 
< 0.1%
976
 
< 0.1%
9422
 
< 0.1%
24643
 
10.2%
11431
 
3.1%
-12
 
< 0.1%

HLCNOTYR
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.847806
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:10.989057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median2
Q32
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.646313
Coefficient of variation (CV)2.3853343
Kurtosis3.9932483
Mean12.847806
Median Absolute Deviation (MAD)0
Skewness2.4476725
Sum587376
Variance939.19647
MonotonicityNot monotonic
2026-02-16T05:56:11.087728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
237287
81.6%
994643
 
10.2%
13270
 
7.2%
98347
 
0.8%
9490
 
0.2%
9758
 
0.1%
8521
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
13270
 
7.2%
237287
81.6%
8521
 
< 0.1%
9490
 
0.2%
9758
 
0.1%
98347
 
0.8%
994643
 
10.2%
ValueCountFrequency (%)
994643
 
10.2%
98347
 
0.8%
9758
 
0.1%
9490
 
0.2%
8521
 
< 0.1%
237287
81.6%
13270
 
7.2%
-12
 
< 0.1%

HLCNOTMO
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.27289
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:11.205598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile6
Q199
median99
Q399
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.257115
Coefficient of variation (CV)0.26288453
Kurtosis9.1950425
Mean92.27289
Median Absolute Deviation (MAD)0
Skewness-3.3427554
Sum4218532
Variance588.40763
MonotonicityNot monotonic
2026-02-16T05:56:11.320441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
9941930
91.7%
1644
 
1.4%
2479
 
1.0%
98456
 
1.0%
3448
 
1.0%
6355
 
0.8%
4266
 
0.6%
5210
 
0.5%
8173
 
0.4%
12159
 
0.3%
Other values (8)598
 
1.3%
ValueCountFrequency (%)
-12
 
< 0.1%
1644
1.4%
2479
1.0%
3448
1.0%
4266
0.6%
5210
 
0.5%
6355
0.8%
798
 
0.2%
8173
 
0.4%
9145
 
0.3%
ValueCountFrequency (%)
9941930
91.7%
98456
 
1.0%
9759
 
0.1%
9416
 
< 0.1%
852
 
< 0.1%
12159
 
0.3%
11146
 
0.3%
10130
 
0.3%
9145
 
0.3%
8173
 
0.4%

HLCLAST
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.323352
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:11.415079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile3
Q199
median99
Q399
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation28.853064
Coefficient of variation (CV)0.32301815
Kurtosis5.0381488
Mean89.323352
Median Absolute Deviation (MAD)0
Skewness-2.6523492
Sum4083685
Variance832.49931
MonotonicityNot monotonic
2026-02-16T05:56:11.521542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
9940649
88.9%
41315
 
2.9%
3958
 
2.1%
5904
 
2.0%
1872
 
1.9%
2559
 
1.2%
98368
 
0.8%
9758
 
0.1%
9433
 
0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
1872
 
1.9%
2559
 
1.2%
3958
 
2.1%
41315
 
2.9%
5904
 
2.0%
9433
 
0.1%
9758
 
0.1%
98368
 
0.8%
9940649
88.9%
ValueCountFrequency (%)
9940649
88.9%
98368
 
0.8%
9758
 
0.1%
9433
 
0.1%
5904
 
2.0%
41315
 
2.9%
3958
 
2.1%
2559
 
1.2%
1872
 
1.9%
-12
 
< 0.1%

HLLOSRSN
Real number (ℝ)

High correlation 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.31819
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:11.610435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile4
Q199
median99
Q399
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25.805476
Coefficient of variation (CV)0.28258857
Kurtosis7.459764
Mean91.31819
Median Absolute Deviation (MAD)0
Skewness-3.072914
Sum4174885
Variance665.92262
MonotonicityNot monotonic
2026-02-16T05:56:11.716093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
9941553
90.9%
4972
 
2.1%
1755
 
1.7%
5397
 
0.9%
98368
 
0.8%
3342
 
0.7%
2305
 
0.7%
6245
 
0.5%
12224
 
0.5%
10174
 
0.4%
Other values (8)383
 
0.8%
ValueCountFrequency (%)
-12
 
< 0.1%
1755
1.7%
2305
 
0.7%
3342
 
0.7%
4972
2.1%
5397
0.9%
6245
 
0.5%
763
 
0.1%
821
 
< 0.1%
951
 
0.1%
ValueCountFrequency (%)
9941553
90.9%
98368
 
0.8%
9762
 
0.1%
9420
 
< 0.1%
852
 
< 0.1%
12224
 
0.5%
11162
 
0.4%
10174
 
0.4%
951
 
0.1%
821
 
< 0.1%

HLNVCOST
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.102432
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:11.843022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile99
Q199
median99
Q399
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.293939
Coefficient of variation (CV)0.13690634
Kurtosis45.786839
Mean97.102432
Median Absolute Deviation (MAD)0
Skewness-6.9097423
Sum4439329
Variance176.72882
MonotonicityNot monotonic
2026-02-16T05:56:11.968871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9944378
97.1%
1451
 
1.0%
6450
 
1.0%
98378
 
0.8%
9756
 
0.1%
943
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
1451
 
1.0%
6450
 
1.0%
943
 
< 0.1%
9756
 
0.1%
98378
 
0.8%
9944378
97.1%
ValueCountFrequency (%)
9944378
97.1%
98378
 
0.8%
9756
 
0.1%
943
 
< 0.1%
6450
 
1.0%
1451
 
1.0%
-12
 
< 0.1%

HLNVOFFR
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.136883
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:12.042542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile99
Q199
median99
Q399
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.04907
Coefficient of variation (CV)0.13433692
Kurtosis45.725148
Mean97.136883
Median Absolute Deviation (MAD)0
Skewness-6.9063528
Sum4440904
Variance170.27822
MonotonicityNot monotonic
2026-02-16T05:56:12.118273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9944378
97.1%
6765
 
1.7%
98378
 
0.8%
1136
 
0.3%
9756
 
0.1%
943
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
1136
 
0.3%
6765
 
1.7%
943
 
< 0.1%
9756
 
0.1%
98378
 
0.8%
9944378
97.1%
ValueCountFrequency (%)
9944378
97.1%
98378
 
0.8%
9756
 
0.1%
943
 
< 0.1%
6765
 
1.7%
1136
 
0.3%
-12
 
< 0.1%

HLNVREF
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.148804
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:12.189480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile99
Q199
median99
Q399
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.963239
Coefficient of variation (CV)0.13343694
Kurtosis45.666378
Mean97.148804
Median Absolute Deviation (MAD)0
Skewness-6.9032208
Sum4441449
Variance168.04556
MonotonicityNot monotonic
2026-02-16T05:56:12.272213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9944378
97.1%
6874
 
1.9%
98378
 
0.8%
9756
 
0.1%
127
 
0.1%
943
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
127
 
0.1%
6874
 
1.9%
943
 
< 0.1%
9756
 
0.1%
98378
 
0.8%
9944378
97.1%
ValueCountFrequency (%)
9944378
97.1%
98378
 
0.8%
9756
 
0.1%
943
 
< 0.1%
6874
 
1.9%
127
 
0.1%
-12
 
< 0.1%

HLNVNEED
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.132071
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:12.369778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile99
Q199
median99
Q399
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.083555
Coefficient of variation (CV)0.13469861
Kurtosis45.743073
Mean97.132071
Median Absolute Deviation (MAD)0
Skewness-6.9073166
Sum4440684
Variance171.1794
MonotonicityNot monotonic
2026-02-16T05:56:12.432354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9944378
97.1%
6721
 
1.6%
98378
 
0.8%
1180
 
0.4%
9756
 
0.1%
943
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
1180
 
0.4%
6721
 
1.6%
943
 
< 0.1%
9756
 
0.1%
98378
 
0.8%
9944378
97.1%
ValueCountFrequency (%)
9944378
97.1%
98378
 
0.8%
9756
 
0.1%
943
 
< 0.1%
6721
 
1.6%
1180
 
0.4%
-12
 
< 0.1%

HLNVSOR
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.137211
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:12.499159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile99
Q199
median99
Q399
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.046715
Coefficient of variation (CV)0.13431223
Kurtosis45.723807
Mean97.137211
Median Absolute Deviation (MAD)0
Skewness-6.906281
Sum4440919
Variance170.21677
MonotonicityNot monotonic
2026-02-16T05:56:12.616876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9944378
97.1%
6768
 
1.7%
98378
 
0.8%
1133
 
0.3%
9756
 
0.1%
943
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
1133
 
0.3%
6768
 
1.7%
943
 
< 0.1%
9756
 
0.1%
98378
 
0.8%
9944378
97.1%
ValueCountFrequency (%)
9944378
97.1%
98378
 
0.8%
9756
 
0.1%
943
 
< 0.1%
6768
 
1.7%
1133
 
0.3%
-12
 
< 0.1%

IRMCDCHP
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
35320 
1
10396 
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
235320
77.3%
110396
 
22.7%
-12
 
< 0.1%

Length

2026-02-16T05:56:12.773229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:12.825387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
235320
77.3%
110398
 
22.7%

Most occurring characters

ValueCountFrequency (%)
235320
77.3%
110398
 
22.7%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
235320
77.3%
110398
 
22.7%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
235320
77.3%
110398
 
22.7%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
235320
77.3%
110398
 
22.7%
-2
 
< 0.1%

IIMCDCHP
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
45278 
3
 
438
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145278
99.0%
3438
 
1.0%
-12
 
< 0.1%

Length

2026-02-16T05:56:12.928353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:13.026434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
145280
99.0%
3438
 
1.0%

Most occurring characters

ValueCountFrequency (%)
145280
99.0%
3438
 
1.0%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
145280
99.0%
3438
 
1.0%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
145280
99.0%
3438
 
1.0%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
145280
99.0%
3438
 
1.0%
-2
 
< 0.1%

IRMEDICR
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
41915 
1
 
3801
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
241915
91.7%
13801
 
8.3%
-12
 
< 0.1%

Length

2026-02-16T05:56:13.142951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:13.286477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
241915
91.7%
13803
 
8.3%

Most occurring characters

ValueCountFrequency (%)
241915
91.7%
13803
 
8.3%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
241915
91.7%
13803
 
8.3%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
241915
91.7%
13803
 
8.3%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
241915
91.7%
13803
 
8.3%
-2
 
< 0.1%

IIMEDICR
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
45492 
3
 
224
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145492
99.5%
3224
 
0.5%
-12
 
< 0.1%

Length

2026-02-16T05:56:13.377680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:13.466064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
145494
99.5%
3224
 
0.5%

Most occurring characters

ValueCountFrequency (%)
145494
99.5%
3224
 
0.5%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
145494
99.5%
3224
 
0.5%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
145494
99.5%
3224
 
0.5%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
145494
99.5%
3224
 
0.5%
-2
 
< 0.1%

IRCHMPUS
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
43975 
1
 
1741
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
243975
96.2%
11741
 
3.8%
-12
 
< 0.1%

Length

2026-02-16T05:56:13.548519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:13.633302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
243975
96.2%
11743
 
3.8%

Most occurring characters

ValueCountFrequency (%)
243975
96.2%
11743
 
3.8%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
243975
96.2%
11743
 
3.8%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
243975
96.2%
11743
 
3.8%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
243975
96.2%
11743
 
3.8%
-2
 
< 0.1%

IICHMPUS
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
45587 
3
 
129
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145587
99.7%
3129
 
0.3%
-12
 
< 0.1%

Length

2026-02-16T05:56:13.755493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:13.837872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
145589
99.7%
3129
 
0.3%

Most occurring characters

ValueCountFrequency (%)
145589
99.7%
3129
 
0.3%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
145589
99.7%
3129
 
0.3%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
145589
99.7%
3129
 
0.3%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
145589
99.7%
3129
 
0.3%
-2
 
< 0.1%

IRPRVHLT
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
27998 
2
17718 
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
127998
61.2%
217718
38.8%
-12
 
< 0.1%

Length

2026-02-16T05:56:13.918300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:13.973673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
128000
61.2%
217718
38.8%

Most occurring characters

ValueCountFrequency (%)
128000
61.2%
217718
38.8%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
128000
61.2%
217718
38.8%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
128000
61.2%
217718
38.8%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
128000
61.2%
217718
38.8%
-2
 
< 0.1%

IIPRVHLT
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
45394 
3
 
322
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145394
99.3%
3322
 
0.7%
-12
 
< 0.1%

Length

2026-02-16T05:56:14.042004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:14.091180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
145396
99.3%
3322
 
0.7%

Most occurring characters

ValueCountFrequency (%)
145396
99.3%
3322
 
0.7%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
145396
99.3%
3322
 
0.7%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
145396
99.3%
3322
 
0.7%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
145396
99.3%
3322
 
0.7%
-2
 
< 0.1%

IROTHHLT
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
99
39493 
2
4755 
1
 
1468
-1
 
2

Length

Max length2
Median length2
Mean length1.8638829
Min length1

Characters and Unicode

Total characters85213
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row99
2nd row99
3rd row99
4th row99
5th row99

Common Values

ValueCountFrequency (%)
9939493
86.4%
24755
 
10.4%
11468
 
3.2%
-12
 
< 0.1%

Length

2026-02-16T05:56:14.167275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:14.247059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9939493
86.4%
24755
 
10.4%
11470
 
3.2%

Most occurring characters

ValueCountFrequency (%)
978986
92.7%
24755
 
5.6%
11470
 
1.7%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)85213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
978986
92.7%
24755
 
5.6%
11470
 
1.7%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)85213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
978986
92.7%
24755
 
5.6%
11470
 
1.7%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)85213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
978986
92.7%
24755
 
5.6%
11470
 
1.7%
-2
 
< 0.1%

IIOTHHLT
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
9
39218 
1
6074 
3
 
424
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row9
3rd row9
4th row9
5th row9

Common Values

ValueCountFrequency (%)
939218
85.8%
16074
 
13.3%
3424
 
0.9%
-12
 
< 0.1%

Length

2026-02-16T05:56:14.318873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:14.415960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
939218
85.8%
16076
 
13.3%
3424
 
0.9%

Most occurring characters

ValueCountFrequency (%)
939218
85.8%
16076
 
13.3%
3424
 
0.9%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
939218
85.8%
16076
 
13.3%
3424
 
0.9%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
939218
85.8%
16076
 
13.3%
3424
 
0.9%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
939218
85.8%
16076
 
13.3%
3424
 
0.9%
-2
 
< 0.1%

HLCALLFG
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
98
45707 
1
 
9
-1
 
2

Length

Max length2
Median length2
Mean length1.9998031
Min length1

Characters and Unicode

Total characters91427
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row98
2nd row98
3rd row98
4th row98
5th row98

Common Values

ValueCountFrequency (%)
9845707
> 99.9%
19
 
< 0.1%
-12
 
< 0.1%

Length

2026-02-16T05:56:14.511550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:14.577452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9845707
> 99.9%
111
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
945707
50.0%
845707
50.0%
111
 
< 0.1%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)91427
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
945707
50.0%
845707
50.0%
111
 
< 0.1%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)91427
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
945707
50.0%
845707
50.0%
111
 
< 0.1%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)91427
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
945707
50.0%
845707
50.0%
111
 
< 0.1%
-2
 
< 0.1%

HLCALL99
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
98
45707 
1
 
9
-1
 
2

Length

Max length2
Median length2
Mean length1.9998031
Min length1

Characters and Unicode

Total characters91427
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row98
2nd row98
3rd row98
4th row98
5th row98

Common Values

ValueCountFrequency (%)
9845707
> 99.9%
19
 
< 0.1%
-12
 
< 0.1%

Length

2026-02-16T05:56:14.659984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:14.723141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9845707
> 99.9%
111
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
945707
50.0%
845707
50.0%
111
 
< 0.1%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)91427
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
945707
50.0%
845707
50.0%
111
 
< 0.1%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)91427
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
945707
50.0%
845707
50.0%
111
 
< 0.1%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)91427
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
945707
50.0%
845707
50.0%
111
 
< 0.1%
-2
 
< 0.1%

ANYHLTI2
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9714992
Minimum-1
Maximum98
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:14.816920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum98
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.9888045
Coefficient of variation (CV)4.5593752
Kurtosis102.71719
Mean1.9714992
Median Absolute Deviation (MAD)0
Skewness10.225625
Sum90133
Variance80.798606
MonotonicityNot monotonic
2026-02-16T05:56:14.951555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
140649
88.9%
24643
 
10.2%
94324
 
0.7%
9756
 
0.1%
9844
 
0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
140649
88.9%
24643
 
10.2%
94324
 
0.7%
9756
 
0.1%
9844
 
0.1%
ValueCountFrequency (%)
9844
 
0.1%
9756
 
0.1%
94324
 
0.7%
24643
 
10.2%
140649
88.9%
-12
 
< 0.1%

IRINSUR4
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
40961 
2
4755 
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
140961
89.6%
24755
 
10.4%
-12
 
< 0.1%

Length

2026-02-16T05:56:15.106560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:15.224817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
140963
89.6%
24755
 
10.4%

Most occurring characters

ValueCountFrequency (%)
140963
89.6%
24755
 
10.4%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
140963
89.6%
24755
 
10.4%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
140963
89.6%
24755
 
10.4%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
140963
89.6%
24755
 
10.4%
-2
 
< 0.1%

IIINSUR4
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
45292 
3
 
424
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145292
99.1%
3424
 
0.9%
-12
 
< 0.1%

Length

2026-02-16T05:56:15.337771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:15.449321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
145294
99.1%
3424
 
0.9%

Most occurring characters

ValueCountFrequency (%)
145294
99.1%
3424
 
0.9%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
145294
99.1%
3424
 
0.9%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
145294
99.1%
3424
 
0.9%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
145294
99.1%
3424
 
0.9%
-2
 
< 0.1%

OTHINS
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
39095 
1
6621 
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
239095
85.5%
16621
 
14.5%
-12
 
< 0.1%

Length

2026-02-16T05:56:15.544658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:15.618495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
239095
85.5%
16623
 
14.5%

Most occurring characters

ValueCountFrequency (%)
239095
85.5%
16623
 
14.5%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
239095
85.5%
16623
 
14.5%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
239095
85.5%
16623
 
14.5%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
239095
85.5%
16623
 
14.5%
-2
 
< 0.1%

CELLNOTCL
Real number (ℝ)

Skewed 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7411742
Minimum-1
Maximum98
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:15.678024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median2
Q32
95-th percentile2
Maximum98
Range99
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.0714422
Coefficient of variation (CV)2.3383314
Kurtosis525.53286
Mean1.7411742
Median Absolute Deviation (MAD)0
Skewness22.784714
Sum79603
Variance16.576641
MonotonicityNot monotonic
2026-02-16T05:56:15.786522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
225919
56.7%
119713
43.1%
9733
 
0.1%
9826
 
0.1%
9420
 
< 0.1%
855
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
119713
43.1%
225919
56.7%
855
 
< 0.1%
9420
 
< 0.1%
9733
 
0.1%
9826
 
0.1%
ValueCountFrequency (%)
9826
 
0.1%
9733
 
0.1%
9420
 
< 0.1%
855
 
< 0.1%
225919
56.7%
119713
43.1%
-12
 
< 0.1%

CELLWRKNG
Real number (ℝ)

Skewed 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1448226
Minimum-1
Maximum98
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:15.912514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum98
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.4214765
Coefficient of variation (CV)2.9886521
Kurtosis771.01767
Mean1.1448226
Median Absolute Deviation (MAD)0
Skewness27.761207
Sum52339
Variance11.706502
MonotonicityNot monotonic
2026-02-16T05:56:16.046738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
144647
97.7%
21010
 
2.2%
9826
 
0.1%
9723
 
0.1%
945
 
< 0.1%
855
 
< 0.1%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
144647
97.7%
21010
 
2.2%
855
 
< 0.1%
945
 
< 0.1%
9723
 
0.1%
9826
 
0.1%
ValueCountFrequency (%)
9826
 
0.1%
9723
 
0.1%
945
 
< 0.1%
855
 
< 0.1%
21010
 
2.2%
144647
97.7%
-12
 
< 0.1%

IRFAMSOC
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
38313 
1
7403 
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
238313
83.8%
17403
 
16.2%
-12
 
< 0.1%

Length

2026-02-16T05:56:16.135920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:16.224324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
238313
83.8%
17405
 
16.2%

Most occurring characters

ValueCountFrequency (%)
238313
83.8%
17405
 
16.2%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
238313
83.8%
17405
 
16.2%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
238313
83.8%
17405
 
16.2%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
238313
83.8%
17405
 
16.2%
-2
 
< 0.1%

IIFAMSOC
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
45285 
3
 
431
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145285
99.1%
3431
 
0.9%
-12
 
< 0.1%

Length

2026-02-16T05:56:16.322549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:16.389535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
145287
99.1%
3431
 
0.9%

Most occurring characters

ValueCountFrequency (%)
145287
99.1%
3431
 
0.9%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
145287
99.1%
3431
 
0.9%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
145287
99.1%
3431
 
0.9%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
145287
99.1%
3431
 
0.9%
-2
 
< 0.1%

IRFAMSSI
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
42595 
1
 
3121
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
242595
93.2%
13121
 
6.8%
-12
 
< 0.1%

Length

2026-02-16T05:56:16.496132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:16.604929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
242595
93.2%
13123
 
6.8%

Most occurring characters

ValueCountFrequency (%)
242595
93.2%
13123
 
6.8%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
242595
93.2%
13123
 
6.8%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
242595
93.2%
13123
 
6.8%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
242595
93.2%
13123
 
6.8%
-2
 
< 0.1%

IIFAMSSI
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
45282 
3
 
434
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145282
99.0%
3434
 
0.9%
-12
 
< 0.1%

Length

2026-02-16T05:56:16.718852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:16.802625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
145284
99.1%
3434
 
0.9%

Most occurring characters

ValueCountFrequency (%)
145284
99.0%
3434
 
0.9%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
145284
99.0%
3434
 
0.9%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
145284
99.0%
3434
 
0.9%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
145284
99.0%
3434
 
0.9%
-2
 
< 0.1%

IRFSTAMP
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
36580 
1
9136 
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
236580
80.0%
19136
 
20.0%
-12
 
< 0.1%

Length

2026-02-16T05:56:16.927326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:17.020215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
236580
80.0%
19138
 
20.0%

Most occurring characters

ValueCountFrequency (%)
236580
80.0%
19138
 
20.0%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
236580
80.0%
19138
 
20.0%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
236580
80.0%
19138
 
20.0%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
236580
80.0%
19138
 
20.0%
-2
 
< 0.1%

IIFSTAMP
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
45487 
3
 
229
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145487
99.5%
3229
 
0.5%
-12
 
< 0.1%

Length

2026-02-16T05:56:17.116703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:17.223306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
145489
99.5%
3229
 
0.5%

Most occurring characters

ValueCountFrequency (%)
145489
99.5%
3229
 
0.5%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
145489
99.5%
3229
 
0.5%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
145489
99.5%
3229
 
0.5%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
145489
99.5%
3229
 
0.5%
-2
 
< 0.1%

IRFAMPMT
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
44497 
1
 
1219
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
244497
97.3%
11219
 
2.7%
-12
 
< 0.1%

Length

2026-02-16T05:56:17.336318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:17.451897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
244497
97.3%
11221
 
2.7%

Most occurring characters

ValueCountFrequency (%)
244497
97.3%
11221
 
2.7%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
244497
97.3%
11221
 
2.7%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
244497
97.3%
11221
 
2.7%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
244497
97.3%
11221
 
2.7%
-2
 
< 0.1%

IIFAMPMT
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
45376 
3
 
340
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145376
99.3%
3340
 
0.7%
-12
 
< 0.1%

Length

2026-02-16T05:56:17.555831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:17.676764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
145378
99.3%
3340
 
0.7%

Most occurring characters

ValueCountFrequency (%)
145378
99.3%
3340
 
0.7%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
145378
99.3%
3340
 
0.7%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
145378
99.3%
3340
 
0.7%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
145378
99.3%
3340
 
0.7%
-2
 
< 0.1%

IRFAMSVC
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
44069 
1
 
1647
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
244069
96.4%
11647
 
3.6%
-12
 
< 0.1%

Length

2026-02-16T05:56:17.788119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:17.888270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
244069
96.4%
11649
 
3.6%

Most occurring characters

ValueCountFrequency (%)
244069
96.4%
11649
 
3.6%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
244069
96.4%
11649
 
3.6%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
244069
96.4%
11649
 
3.6%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
244069
96.4%
11649
 
3.6%
-2
 
< 0.1%

IIFAMSVC
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
45457 
3
 
259
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
145457
99.4%
3259
 
0.6%
-12
 
< 0.1%

Length

2026-02-16T05:56:18.006166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:18.205384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
145459
99.4%
3259
 
0.6%

Most occurring characters

ValueCountFrequency (%)
145459
99.4%
3259
 
0.6%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
145459
99.4%
3259
 
0.6%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
145459
99.4%
3259
 
0.6%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
145459
99.4%
3259
 
0.6%
-2
 
< 0.1%

IRWELMOS
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.126887
Minimum-1
Maximum99
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:18.300436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile12
Q199
median99
Q399
95-th percentile99
Maximum99
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.472772
Coefficient of variation (CV)0.21750185
Kurtosis13.84295
Mean94.126887
Median Absolute Deviation (MAD)0
Skewness-3.9740676
Sum4303293
Variance419.13439
MonotonicityNot monotonic
2026-02-16T05:56:18.462402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
9943261
94.6%
121287
 
2.8%
1238
 
0.5%
6188
 
0.4%
2170
 
0.4%
3151
 
0.3%
4117
 
0.3%
572
 
0.2%
864
 
0.1%
963
 
0.1%
Other values (4)107
 
0.2%
ValueCountFrequency (%)
-12
 
< 0.1%
1238
0.5%
2170
0.4%
3151
0.3%
4117
0.3%
572
 
0.2%
6188
0.4%
743
 
0.1%
864
 
0.1%
963
 
0.1%
ValueCountFrequency (%)
9943261
94.6%
121287
 
2.8%
1125
 
0.1%
1037
 
0.1%
963
 
0.1%
864
 
0.1%
743
 
0.1%
6188
 
0.4%
572
 
0.2%
4117
 
0.3%

IIWELMOS
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
9
42887 
1
 
2367
3
 
462
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row9
3rd row9
4th row9
5th row1

Common Values

ValueCountFrequency (%)
942887
93.8%
12367
 
5.2%
3462
 
1.0%
-12
 
< 0.1%

Length

2026-02-16T05:56:18.605261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:18.697312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
942887
93.8%
12369
 
5.2%
3462
 
1.0%

Most occurring characters

ValueCountFrequency (%)
942887
93.8%
12369
 
5.2%
3462
 
1.0%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
942887
93.8%
12369
 
5.2%
3462
 
1.0%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
942887
93.8%
12369
 
5.2%
3462
 
1.0%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
942887
93.8%
12369
 
5.2%
3462
 
1.0%
-2
 
< 0.1%

IRPINC3
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5846494
Minimum-1
Maximum7
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:18.766242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median2
Q34
95-th percentile7
Maximum7
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9857968
Coefficient of variation (CV)0.76830412
Kurtosis-0.32477971
Mean2.5846494
Median Absolute Deviation (MAD)1
Skewness1.0221726
Sum118165
Variance3.9433888
MonotonicityNot monotonic
2026-02-16T05:56:18.879464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
121564
47.2%
27158
 
15.7%
34536
 
9.9%
63318
 
7.3%
43291
 
7.2%
73181
 
7.0%
52668
 
5.8%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
121564
47.2%
27158
 
15.7%
34536
 
9.9%
43291
 
7.2%
52668
 
5.8%
63318
 
7.3%
73181
 
7.0%
ValueCountFrequency (%)
73181
 
7.0%
63318
 
7.3%
52668
 
5.8%
43291
 
7.2%
34536
 
9.9%
27158
 
15.7%
121564
47.2%
-12
 
< 0.1%

IRFAMIN3
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7504703
Minimum-1
Maximum7
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:19.000359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median5
Q37
95-th percentile7
Maximum7
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1067253
Coefficient of variation (CV)0.4434772
Kurtosis-1.261816
Mean4.7504703
Median Absolute Deviation (MAD)2
Skewness-0.41859892
Sum217182
Variance4.4382913
MonotonicityNot monotonic
2026-02-16T05:56:19.087316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
714636
32.0%
67126
15.6%
25572
 
12.2%
35032
 
11.0%
44774
 
10.4%
54516
 
9.9%
14060
 
8.9%
-12
 
< 0.1%
ValueCountFrequency (%)
-12
 
< 0.1%
14060
 
8.9%
25572
 
12.2%
35032
 
11.0%
44774
 
10.4%
54516
 
9.9%
67126
15.6%
714636
32.0%
ValueCountFrequency (%)
714636
32.0%
67126
15.6%
54516
 
9.9%
44774
 
10.4%
35032
 
11.0%
25572
 
12.2%
14060
 
8.9%
-12
 
< 0.1%

IIPINC3
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
44310 
3
 
1406
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
144310
96.9%
31406
 
3.1%
-12
 
< 0.1%

Length

2026-02-16T05:56:19.202138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:19.305088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
144312
96.9%
31406
 
3.1%

Most occurring characters

ValueCountFrequency (%)
144312
96.9%
31406
 
3.1%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
144312
96.9%
31406
 
3.1%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
144312
96.9%
31406
 
3.1%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
144312
96.9%
31406
 
3.1%
-2
 
< 0.1%

IIFAMIN3
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
41279 
3
4437 
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
141279
90.3%
34437
 
9.7%
-12
 
< 0.1%

Length

2026-02-16T05:56:19.437172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:19.536822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
141281
90.3%
34437
 
9.7%

Most occurring characters

ValueCountFrequency (%)
141281
90.3%
34437
 
9.7%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
141281
90.3%
34437
 
9.7%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
141281
90.3%
34437
 
9.7%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
141281
90.3%
34437
 
9.7%
-2
 
< 0.1%

GOVTPROG
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
34872 
1
10844 
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
234872
76.3%
110844
 
23.7%
-12
 
< 0.1%

Length

2026-02-16T05:56:19.639661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:19.715289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
234872
76.3%
110846
 
23.7%

Most occurring characters

ValueCountFrequency (%)
234872
76.3%
110846
 
23.7%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
234872
76.3%
110846
 
23.7%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
234872
76.3%
110846
 
23.7%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
234872
76.3%
110846
 
23.7%
-2
 
< 0.1%

POVERTY3
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
3
25819 
2
10225 
1
9331 
-1
 
343

Length

Max length2
Median length1
Mean length1.0075025
Min length1

Characters and Unicode

Total characters46061
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
325819
56.5%
210225
 
22.4%
19331
 
20.4%
-1343
 
0.8%

Length

2026-02-16T05:56:19.819758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:19.897007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
325819
56.5%
210225
 
22.4%
19674
 
21.2%

Most occurring characters

ValueCountFrequency (%)
325819
56.1%
210225
 
22.2%
19674
 
21.0%
-343
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)46061
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
325819
56.1%
210225
 
22.2%
19674
 
21.0%
-343
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)46061
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
325819
56.1%
210225
 
22.2%
19674
 
21.0%
-343
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)46061
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
325819
56.1%
210225
 
22.2%
19674
 
21.0%
-343
 
0.7%

TOOLONG
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
42270 
1
 
3307
98
 
139
-1
 
2

Length

Max length2
Median length1
Mean length1.0030841
Min length1

Characters and Unicode

Total characters45859
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
242270
92.5%
13307
 
7.2%
98139
 
0.3%
-12
 
< 0.1%

Length

2026-02-16T05:56:20.044539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:20.113625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
242270
92.5%
13309
 
7.2%
98139
 
0.3%

Most occurring characters

ValueCountFrequency (%)
242270
92.2%
13309
 
7.2%
9139
 
0.3%
8139
 
0.3%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45859
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
242270
92.2%
13309
 
7.2%
9139
 
0.3%
8139
 
0.3%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45859
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
242270
92.2%
13309
 
7.2%
9139
 
0.3%
8139
 
0.3%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45859
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
242270
92.2%
13309
 
7.2%
9139
 
0.3%
8139
 
0.3%
-2
 
< 0.1%

TROUBUND
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
42980 
1
 
2597
98
 
139
-1
 
2

Length

Max length2
Median length1
Mean length1.0030841
Min length1

Characters and Unicode

Total characters45859
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
242980
94.0%
12597
 
5.7%
98139
 
0.3%
-12
 
< 0.1%

Length

2026-02-16T05:56:20.224675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:20.308700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
242980
94.0%
12599
 
5.7%
98139
 
0.3%

Most occurring characters

ValueCountFrequency (%)
242980
93.7%
12599
 
5.7%
9139
 
0.3%
8139
 
0.3%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45859
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
242980
93.7%
12599
 
5.7%
9139
 
0.3%
8139
 
0.3%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45859
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
242980
93.7%
12599
 
5.7%
9139
 
0.3%
8139
 
0.3%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45859
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
242980
93.7%
12599
 
5.7%
9139
 
0.3%
8139
 
0.3%
-2
 
< 0.1%

PDEN10
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
22526 
1
19681 
3
3509 
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
222526
49.3%
119681
43.0%
33509
 
7.7%
-12
 
< 0.1%

Length

2026-02-16T05:56:20.421033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:20.525976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
222526
49.3%
119683
43.1%
33509
 
7.7%

Most occurring characters

ValueCountFrequency (%)
222526
49.3%
119683
43.1%
33509
 
7.7%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
222526
49.3%
119683
43.1%
33509
 
7.7%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
222526
49.3%
119683
43.1%
33509
 
7.7%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
222526
49.3%
119683
43.1%
33509
 
7.7%
-2
 
< 0.1%

COUTYP2
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
20236 
2
15997 
3
9483 
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
120236
44.3%
215997
35.0%
39483
20.7%
-12
 
< 0.1%

Length

2026-02-16T05:56:20.630811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:20.757630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
120238
44.3%
215997
35.0%
39483
20.7%

Most occurring characters

ValueCountFrequency (%)
120238
44.3%
215997
35.0%
39483
20.7%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
120238
44.3%
215997
35.0%
39483
20.7%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
120238
44.3%
215997
35.0%
39483
20.7%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
120238
44.3%
215997
35.0%
39483
20.7%
-2
 
< 0.1%

MAIIN102
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
44759 
1
 
957
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
244759
97.9%
1957
 
2.1%
-12
 
< 0.1%

Length

2026-02-16T05:56:20.921080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:21.074623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
244759
97.9%
1959
 
2.1%

Most occurring characters

ValueCountFrequency (%)
244759
97.9%
1959
 
2.1%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
244759
97.9%
1959
 
2.1%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
244759
97.9%
1959
 
2.1%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
244759
97.9%
1959
 
2.1%
-2
 
< 0.1%

AIIND102
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
2
44750 
1
 
966
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
244750
97.9%
1966
 
2.1%
-12
 
< 0.1%

Length

2026-02-16T05:56:21.254647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:21.396426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
244750
97.9%
1968
 
2.1%

Most occurring characters

ValueCountFrequency (%)
244750
97.9%
1968
 
2.1%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
244750
97.9%
1968
 
2.1%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
244750
97.9%
1968
 
2.1%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
244750
97.9%
1968
 
2.1%
-2
 
< 0.1%

ANALWT_C
Real number (ℝ)

Distinct45647
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4692.6612
Minimum-1
Maximum109100.62
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:21.566480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile309.34655
Q11252.3965
median2719.3352
Q35765.8108
95-th percentile15949.605
Maximum109100.62
Range109101.62
Interquartile range (IQR)4513.4143

Descriptive statistics

Standard deviation5724.6595
Coefficient of variation (CV)1.2199175
Kurtosis15.613558
Mean4692.6612
Median Absolute Deviation (MAD)1835.6761
Skewness3.0625796
Sum2.1453908 × 108
Variance32771726
MonotonicityNot monotonic
2026-02-16T05:56:21.689713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521.44298127
 
< 0.1%
806.82899855
 
< 0.1%
447.25180023
 
< 0.1%
361.99818053
 
< 0.1%
11406.144532
 
< 0.1%
516.00461972
 
< 0.1%
3090.1960462
 
< 0.1%
948.61264022
 
< 0.1%
1801.9974362
 
< 0.1%
16678.976712
 
< 0.1%
Other values (45637)45688
99.9%
ValueCountFrequency (%)
-12
< 0.1%
1.6218322511
< 0.1%
1.8866657791
< 0.1%
2.6746415331
< 0.1%
5.403111921
< 0.1%
5.6322925871
< 0.1%
6.1108161161
< 0.1%
6.6429324391
< 0.1%
8.9572104521
< 0.1%
9.516226021
< 0.1%
ValueCountFrequency (%)
109100.6231
< 0.1%
76179.508751
< 0.1%
74971.928521
< 0.1%
73657.39961
< 0.1%
64176.532291
< 0.1%
62126.677851
< 0.1%
61167.130071
< 0.1%
60519.008391
< 0.1%
60209.958011
< 0.1%
59789.463381
< 0.1%

VESTR
Real number (ℝ)

High correlation  Skewed 

Distinct51
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40023.739
Minimum-1
Maximum40050
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size357.3 KiB
2026-02-16T05:56:21.796935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile40003
Q140013
median40025
Q340039
95-th percentile40048
Maximum40050
Range40051
Interquartile range (IQR)26

Descriptive statistics

Standard deviation265.14043
Coefficient of variation (CV)0.0066245792
Kurtosis22717.583
Mean40023.739
Median Absolute Deviation (MAD)13
Skewness-150.49731
Sum1.8298053 × 109
Variance70299.448
MonotonicityNot monotonic
2026-02-16T05:56:21.904323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400481050
 
2.3%
400401040
 
2.3%
400201032
 
2.3%
400101028
 
2.2%
400451020
 
2.2%
400191017
 
2.2%
400291014
 
2.2%
400051013
 
2.2%
400081001
 
2.2%
40009997
 
2.2%
Other values (41)35506
77.7%
ValueCountFrequency (%)
-12
 
< 0.1%
40001920
2.0%
40002887
1.9%
40003879
1.9%
40004929
2.0%
400051013
2.2%
40006924
2.0%
40007919
2.0%
400081001
2.2%
40009997
2.2%
ValueCountFrequency (%)
40050959
2.1%
40049989
2.2%
400481050
2.3%
40047994
2.2%
40046895
2.0%
400451020
2.2%
40044814
1.8%
40043929
2.0%
40042898
2.0%
40041975
2.1%

VEREP
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
1
23134 
2
22582 
-1
 
2

Length

Max length2
Median length1
Mean length1.0000437
Min length1

Characters and Unicode

Total characters45720
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
123134
50.6%
222582
49.4%
-12
 
< 0.1%

Length

2026-02-16T05:56:21.997649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:22.072578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
123136
50.6%
222582
49.4%

Most occurring characters

ValueCountFrequency (%)
123136
50.6%
222582
49.4%
-2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
123136
50.6%
222582
49.4%
-2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
123136
50.6%
222582
49.4%
-2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45720
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
123136
50.6%
222582
49.4%
-2
 
< 0.1%

Criminal
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size357.3 KiB
0
42543 
1
 
3175

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45718
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
042543
93.1%
13175
 
6.9%

Length

2026-02-16T05:56:22.223625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-16T05:56:22.311218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
042543
93.1%
13175
 
6.9%

Most occurring characters

ValueCountFrequency (%)
042543
93.1%
13175
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)45718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
042543
93.1%
13175
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
042543
93.1%
13175
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
042543
93.1%
13175
 
6.9%

Interactions

2026-02-16T05:56:00.761432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:45.044403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:48.211283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:51.225965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:54.148751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:56.676111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:59.430696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:01.987247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:04.882196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:07.390454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:10.194433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:13.260513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:15.862515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:18.281231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:21.315246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:24.142767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:26.571271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:29.249418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:33.092758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:35.837646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:38.329445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:41.061354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:43.592714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:47.117655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:49.984166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:53.890096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:57.444212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:00.906168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:45.156904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:48.344324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:51.327758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:54.243180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:56.763212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:59.509572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:02.066652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:04.987561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:07.528395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:10.274148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:13.348613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:15.950291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:18.365192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:21.428219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:24.250260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:26.668820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:29.330678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:33.200272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:35.925691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:38.470574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:41.135057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:43.681839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:47.277582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:50.107406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:54.046149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:57.591858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:01.070769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:45.260303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:48.419096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:51.403255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:54.371124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:56.860079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:59.619648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:02.154519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:05.092067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:07.598452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:10.383445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:13.474690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:16.047251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:18.445686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:21.516383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:24.341880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:26.786220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:29.411625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:33.285996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:36.016100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:38.611146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:41.230411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:43.760126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:47.361129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:50.210553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:54.172138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:57.726286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:01.218881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:45.365305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:48.512744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:51.489487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:54.451626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:56.942587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:59.743655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:02.243527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:05.163612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:07.678521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:10.502133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:13.574634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:16.136406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:18.535441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:21.602783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:24.413243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:26.852590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:29.560188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:33.370472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:36.131090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:38.727375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:41.305154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:43.870233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:47.457277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:50.373440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:54.294886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:57.828166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:01.342721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:45.506665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:48.621270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:51.582443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:54.557808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:57.021667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:59.848091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:02.375114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:05.258721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:07.770772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:10.587958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:13.657165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:16.246681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:18.614027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:21.691804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:24.497185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:26.922073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:29.627392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:33.438073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:36.231642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:38.802113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:41.405913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:44.041478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:47.552897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:50.510089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:54.392451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:57.968071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:01.499257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:45.589492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:48.711108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:51.710040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:54.663085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:57.099411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:59.929992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:02.470980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:05.360663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:07.893313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:10.699753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:13.744368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:16.317555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:18.692147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:21.892269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:24.572141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:27.012455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:29.697526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:33.505785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:36.301569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:38.896764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:41.501729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:44.155971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:47.682556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:50.654379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:54.519300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-16T05:55:59.832887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:03.532756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:47.541475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:50.431023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:53.281374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:56.139539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:58.903758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:01.459246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:04.349341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:06.836381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:09.577919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:12.724556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:15.324539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:17.675842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:20.745089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:23.592949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:26.055016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:28.659786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:32.525864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:35.239437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-16T05:55:53.040858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:56.603771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:59.943026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:03.672507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:47.674502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:50.529616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:53.373721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:56.228118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:59.001196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:01.547104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:04.437211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:06.927231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:09.709437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:12.794163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:15.399131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:17.767867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:20.878844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:23.703680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:26.134285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:28.739408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:32.598570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:35.317579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:37.760849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:40.502431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:43.096604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:46.598681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:49.352159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:53.173124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:56.729553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:00.101311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:03.776167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:47.775031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:50.657929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:53.725923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:56.329320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:59.077849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:01.624269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:04.517291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:07.033495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:09.799423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:12.869349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:15.518622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:17.850810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:20.954763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:23.775890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:26.228865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:28.806143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:32.727342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:35.400886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:37.906556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:40.631573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:43.187790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:46.697926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:49.516011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:53.264401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:56.850904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:00.207690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:03.905595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:47.913171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:50.757561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:53.836705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:56.424622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:59.164795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:01.710855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:04.616068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:07.121047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:09.896435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:12.973051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:15.615934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:17.978305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:21.066353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:23.863922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:26.302999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:28.904459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:32.802148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:35.486568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:38.001837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:40.724790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:43.296601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:46.791207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:49.598103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:53.416609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-16T05:56:00.369580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:04.085319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:48.017073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:50.928787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:53.935541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:56.513710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:59.247969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:01.809994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:04.700198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:07.204848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:09.975816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:13.077247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:15.699847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:18.094935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:21.148739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:23.955010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:26.391871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:29.007715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:32.902480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:35.636946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:38.103649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:40.806275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:43.404465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:46.909709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:49.720190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:53.562974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:57.140518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:00.506431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:04.186209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:48.095347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:51.114151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:54.034278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:56.593980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:54:59.334274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:01.898032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:04.783688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:07.300416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:10.081698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:13.172955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:15.780994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:18.200927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:21.234542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:24.056084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:26.468806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:29.142794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:32.989581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:35.733063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:38.203536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:40.934424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:43.493591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:46.995573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:49.859668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:53.733407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:55:57.329210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-16T05:56:00.646607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-16T05:56:22.500262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AIIND102ANALWT_CANYHLTI2CAIDCHIPCELLNOTCLCELLWRKNGCHAMPUSCOUTYP2CriminalGOVTPROGGRPHLTINHLCALL99HLCALLFGHLCLASTHLCNOTMOHLCNOTYRHLLOSRSNHLNVCOSTHLNVNEEDHLNVOFFRHLNVREFHLNVSORHLTINNOSIFATHERIICHMPUSIIFAMIN3IIFAMPMTIIFAMSOCIIFAMSSIIIFAMSVCIIFSTAMPIIHH65_2IIHHSIZ2IIINSUR4IIKI17_2IIMCDCHPIIMEDICRIIOTHHLTIIPINC3IIPRVHLTIIWELMOSIRCHMPUSIRFAMIN3IRFAMPMTIRFAMSOCIRFAMSSIIRFAMSVCIRFSTAMPIRHH65_2IRHHSIZ2IRINSUR4IRKI17_2IRMCDCHPIRMEDICRIROTHHLTIRPINC3IRPRVHLTIRWELMOSMAIIN102MEDICARENRCH17_2OTHINSPDEN10PERIDPOVERTY3PRVHLTINPRXRETRYPRXYDATATOOLONGTROUBUNDVEREPVESTR
AIIND1021.0000.0150.0000.0000.0000.0000.0000.7150.0000.7070.0360.7070.7070.0200.0190.0000.0170.0460.0460.0460.0460.0460.0410.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7080.7070.7070.7070.7070.7080.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7080.7070.7080.0300.9930.0000.1090.7080.7160.0140.0620.0000.0510.0090.7070.7070.7071.000
ANALWT_C0.0151.0000.0140.142-0.1070.031-0.0250.0650.0300.037-0.0480.0000.000-0.0140.0210.019-0.0140.0040.0040.0040.0040.0040.0010.0940.0140.0170.0000.0000.0000.0000.0000.0000.0110.0020.0000.0030.0000.0200.0390.0000.0210.0330.0960.0190.1690.0000.0190.0470.186-0.1540.0150.1080.0690.2310.0180.3420.0340.0580.015-0.2620.0260.1730.066-0.0030.058-0.0780.0360.2970.0440.0260.000-0.013
ANYHLTI20.0000.0141.0000.2170.1120.0370.0980.0180.0490.0140.4100.0000.000-0.995-0.0030.804-0.895-0.504-0.504-0.504-0.504-0.504-0.7810.0460.3910.1310.2890.2270.2210.2660.2580.0870.1801.0000.1020.6320.4901.0000.1210.6410.2660.017-0.1930.0000.0030.0000.0070.0140.0130.0490.0500.0000.0120.0200.060-0.0730.050-0.0080.0000.1360.1380.0070.016-0.0040.0330.449-0.0480.0980.0770.0780.000-0.000
CAIDCHIP0.0000.1420.2171.000-0.054-0.020-0.0470.0160.0500.010-0.5360.0000.000-0.2090.0550.222-0.195-0.151-0.151-0.151-0.151-0.151-0.2010.0330.2880.0880.2850.1590.1630.1880.1860.0740.1330.4490.0920.7070.3350.4500.0870.2860.2480.0000.3660.0000.0000.0000.0060.0080.004-0.1500.0040.0030.0180.0120.0060.3130.0220.2200.0000.0110.1060.0070.015-0.0140.017-0.539-0.0290.1610.0640.0650.000-0.010
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HLNVREF0.0460.004-0.504-0.151-0.059-0.057-0.0860.0510.0380.047-0.1850.0460.0460.4470.130-0.3840.1221.0001.0001.0001.0001.0000.2890.0630.0470.0560.0460.0460.0460.0470.0460.0520.0490.0480.0550.0480.0470.3650.0470.0480.0470.0540.1040.0460.0630.0530.0480.0500.054-0.0590.4180.0570.0900.0630.4180.0600.1840.0010.046-0.1100.0620.0740.0530.0090.123-0.2370.090-0.0330.0600.0570.046-0.003
HLNVSOR0.0460.004-0.504-0.151-0.059-0.057-0.0860.0510.0380.047-0.1850.0460.0460.4470.130-0.3840.1221.0001.0001.0001.0001.0000.2890.0630.0470.0560.0460.0460.0460.0470.0460.0520.0490.0480.0550.0480.0470.3650.0470.0480.0470.0540.1040.0460.0630.0530.0480.0500.054-0.0590.4180.0570.0900.0630.4180.0600.1840.0010.046-0.1100.0620.0740.0530.0090.123-0.2370.090-0.0330.0600.0570.046-0.003
HLTINNOS0.0410.001-0.781-0.201-0.114-0.026-0.0750.0250.1070.068-0.4700.0160.0160.796-0.054-0.6260.6990.2890.2890.2890.2890.2891.0000.1360.0260.0420.0180.0160.0170.0180.0160.0220.0160.0410.0260.0410.0321.0000.0160.0360.0180.0790.2020.0160.0530.0180.0160.0740.062-0.0350.8470.0330.2130.1190.9860.0650.4920.0040.042-0.1120.0590.1020.0310.0020.198-0.4830.022-0.1120.0260.0200.0160.004
IFATHER0.7070.0940.0460.0330.0210.0260.0230.5770.0850.7160.0990.7070.7070.1260.0340.0790.0680.0630.0630.0630.0630.0630.1361.0000.7070.7070.7070.7070.7070.7070.7070.5790.7100.7080.7090.7080.7070.5830.7080.7070.5810.7070.5120.7100.7110.7090.7100.7160.5820.5290.7130.5570.7310.7160.5830.5750.7140.0840.7070.0150.1980.7150.5770.0080.1090.0220.0510.7620.5780.5770.7071.000
IICHMPUS0.7070.0140.3910.2880.2430.2840.7070.7070.0400.7070.1130.7070.7070.0260.0180.0920.0190.0470.0470.0470.0470.0470.0260.7071.0000.7100.7400.7340.7380.7460.7480.7140.7410.7590.7170.7610.7830.7590.7130.7720.7340.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.0290.7070.3450.2070.7070.7070.0110.0540.3230.0680.0130.7150.7150.7071.000
IIFAMIN30.7070.0170.1310.0880.0630.0560.0700.7080.0370.7080.0680.7070.7070.0350.0180.0550.0230.0560.0560.0560.0560.0560.0420.7070.7101.0000.7160.7190.7200.7180.7150.7100.7100.7130.7100.7120.7110.7140.7810.7120.7180.7070.7080.7070.7080.7070.7070.7070.7100.7090.7080.7070.7080.7080.7080.7100.7090.0290.7070.0770.1330.7080.7080.0100.0680.0840.1040.0780.7080.7080.7071.000
IIFAMPMT0.7070.0000.2890.2850.1480.1600.2210.7070.0340.7080.0770.7070.7070.0200.0570.1670.0160.0460.0460.0460.0460.0460.0180.7070.7400.7161.0000.7640.7720.8250.7880.7120.7230.7360.7140.7470.7330.7360.7150.7330.9200.7070.7070.7070.7070.7070.7070.7080.7070.7070.7070.7070.7070.7070.7070.7080.7070.0320.7070.2010.1620.7070.7070.0090.0580.2010.1150.0270.7100.7100.7071.000
IIFAMSOC0.7070.0000.2270.1590.1310.1420.2010.7070.0260.7070.0680.7070.7070.0190.0170.0570.0150.0460.0460.0460.0460.0460.0160.7070.7340.7190.7641.0000.8020.7640.7680.7120.7210.7250.7130.7240.7300.7250.7150.7280.7570.7070.7070.7070.7070.7070.7070.7070.7080.7080.7070.7070.7070.7070.7070.7080.7070.0310.7070.1880.1560.7070.7070.0090.0550.1810.1230.0370.7090.7090.7071.000
IIFAMSSI0.7070.0000.2210.1630.1270.1370.2120.7070.0370.7080.0710.7070.7070.0190.0170.0610.0160.0460.0460.0460.0460.0460.0170.7070.7380.7200.7720.8021.0000.7760.7720.7130.7190.7240.7130.7250.7300.7240.7160.7310.7690.7070.7070.7070.7080.7070.7070.7080.7080.7080.7070.7070.7070.7070.7070.7080.7080.0320.7070.1870.1500.7070.7070.0090.0590.1910.1100.0390.7100.7100.7071.000
IIFAMSVC0.7070.0000.2660.1880.1750.1890.2420.7070.0350.7080.0850.7070.7070.0210.0160.0650.0160.0470.0470.0470.0470.0470.0180.7070.7460.7180.8250.7640.7761.0000.7930.7120.7270.7320.7150.7300.7360.7320.7170.7360.8800.7070.7070.7070.7070.7070.7070.7080.7070.7070.7070.7070.7070.7070.7070.7070.7070.0320.7070.2140.1760.7070.7070.0080.0570.2150.1060.0270.7120.7120.7071.000
IIFSTAMP0.7070.0000.2580.1860.1770.2010.2500.7070.0240.7070.0870.7070.7070.0190.0170.0690.0150.0460.0460.0460.0460.0460.0160.7070.7480.7150.7880.7680.7720.7931.0000.7150.7300.7300.7180.7300.7360.7300.7180.7360.7730.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.0290.7070.2140.1830.7070.7070.0070.0540.2150.0910.0230.7120.7120.7071.000
IIHH65_20.7070.0000.0870.0740.1320.1500.1010.5770.0000.7070.0240.7070.7070.0220.0130.0290.0160.0520.0520.0520.0520.0520.0220.5790.7140.7100.7120.7120.7130.7120.7151.0000.7580.7100.9100.7110.7120.5800.7090.7110.5810.7070.5770.7070.7070.7070.7070.7070.5770.5780.7070.5780.7070.7070.5770.5780.7070.0300.7070.0860.2770.7070.5770.0080.0480.0700.0540.0180.5800.5800.7071.000
IIHHSIZ20.7070.0110.1800.1330.2850.3600.2260.7070.0060.7070.0160.7070.7070.0190.0170.0410.0160.0490.0490.0490.0490.0490.0160.7100.7410.7100.7230.7210.7190.7270.7300.7581.0000.7180.7870.7180.7290.7180.7120.7210.7180.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.0300.7070.1780.5060.7070.7070.0070.0530.1410.0510.0210.7310.7310.7071.000
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IIKI17_20.7070.0000.1020.0920.1620.1900.1230.7070.0000.7070.0220.7070.7070.0260.0160.0380.0180.0550.0550.0550.0550.0550.0260.7090.7170.7100.7140.7130.7130.7150.7180.9100.7870.7111.0000.7120.7140.7110.7100.7120.7120.7070.7070.7070.7070.7070.7070.7070.7070.7080.7070.7070.7070.7070.7070.7070.7070.0310.7070.1030.4140.7070.7070.0080.0570.0840.0510.0170.7130.7130.7071.000
IIMCDCHP0.7070.0030.6320.7070.1400.1660.2810.7070.0480.7070.0760.7070.7070.0380.0520.1880.0260.0480.0480.0480.0480.0480.0410.7080.7610.7120.7470.7240.7250.7300.7300.7110.7180.8370.7121.0000.7780.8370.7120.7590.7410.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7080.7070.0300.7070.3260.1470.7070.7070.0170.0560.2790.0940.0180.7100.7100.7071.000
IIMEDICR0.7070.0000.4900.3350.1880.2210.3370.7070.0370.7070.0930.7070.7070.0300.0190.0980.0210.0470.0470.0470.0470.0470.0320.7070.7830.7110.7330.7300.7300.7360.7360.7120.7290.7870.7140.7781.0000.7870.7120.7600.7300.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.0310.7070.7070.1760.7070.7070.0100.0550.2860.0800.0210.7110.7120.7071.000
IIOTHHLT0.7080.0201.0000.4500.1430.1640.2770.5780.1160.7090.3620.7070.7070.8550.0550.6270.4390.3650.3650.3650.3650.3651.0000.5830.7590.7140.7360.7250.7240.7320.7300.5800.7181.0000.7110.8370.7871.0000.7120.8400.5970.7090.5900.7070.7080.7070.7070.7090.5780.5780.9280.5780.7230.7120.8130.5860.7900.0310.7080.3470.1240.7110.5780.0100.1250.4540.1030.1170.5790.5790.7071.000
IIPINC30.7070.0390.1210.0870.1010.1000.0900.7070.0290.7070.0380.7070.7070.0190.0210.0340.0170.0470.0470.0470.0470.0470.0160.7080.7130.7810.7150.7150.7160.7170.7180.7090.7120.7120.7100.7120.7120.7121.0000.7120.7150.7070.7070.7070.7090.7070.7070.7070.7100.7080.7070.7080.7070.7110.7070.7080.7070.0290.7070.0870.1380.7090.7070.0070.0550.0870.0520.0600.7090.7080.7071.000
IIPRVHLT0.7070.0000.6410.2860.1600.1890.3120.7070.1030.7070.1000.7070.7070.0340.0220.1260.0230.0480.0480.0480.0480.0480.0360.7070.7720.7120.7330.7280.7310.7360.7360.7110.7210.8400.7120.7590.7600.8400.7121.0000.7300.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7080.7070.0290.7070.2820.1530.7070.7070.0080.0560.7070.0980.0220.7100.7100.7071.000
IIWELMOS0.7070.0210.2660.2480.1330.1410.2000.5770.0360.7690.1490.7070.7070.0210.0430.1310.0140.0470.0470.0470.0470.0470.0180.5810.7340.7180.9200.7570.7690.8800.7730.5810.7180.7320.7120.7410.7300.5970.7150.7301.0000.7070.5940.8570.7080.7170.9050.7460.5780.5790.7070.5810.7260.7070.5770.5820.7200.6970.7070.1860.1380.7070.5770.0090.1540.1880.1220.0320.5800.5800.7071.000
IRCHMPUS0.7070.0330.0170.0000.0000.0000.0000.7080.0130.7080.0930.7090.7090.0690.0260.0480.0440.0540.0540.0540.0540.0540.0790.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7090.7070.7070.7071.0000.7070.7070.7100.7070.7070.7080.7100.7080.7090.7080.7090.7110.7090.7080.7130.0340.7070.0000.1110.7850.7080.0070.0640.0000.0510.0190.7070.7070.7071.000
IRFAMIN30.7080.096-0.1930.366-0.229-0.101-0.0210.5800.2720.781-0.4860.7070.7070.1930.090-0.0970.1600.1040.1040.1040.1040.1040.2020.5120.7070.7080.7070.7070.7070.7070.7070.5770.7070.7070.7070.7070.7070.5900.7070.7070.5940.7071.0000.7190.7130.7220.7200.7820.5800.1250.7220.5040.7590.7110.5910.3660.7850.2200.7080.0780.0820.7100.580-0.0070.614-0.471-0.023-0.1020.5790.5780.707-0.016
IRFAMPMT0.7070.0190.0000.0000.0000.0000.0000.7070.0000.7380.1050.7070.7070.0190.0250.0090.0170.0460.0460.0460.0460.0460.0160.7100.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.8570.7070.7191.0000.7070.7130.7320.7330.7070.7090.7070.7120.7190.7070.7070.7110.7150.4930.7070.0000.1200.7070.7070.0110.1560.0000.0510.0190.7070.7070.7071.000
IRFAMSOC0.7070.1690.0030.0000.0000.0000.0000.7090.1110.7130.0630.7070.7070.0480.0270.0310.0250.0630.0630.0630.0630.0630.0530.7110.7070.7080.7070.7070.7080.7070.7070.7070.7070.7070.7070.7070.7070.7080.7090.7070.7080.7100.7130.7071.0000.7240.7070.7090.8400.7180.7080.7200.7080.8090.7080.7130.7100.0400.7070.0000.1450.7670.7080.0080.0750.0000.0510.0560.7100.7090.7071.000
IRFAMSSI0.7070.0000.0000.0000.0000.0000.0000.7070.0000.7860.1330.7070.7070.0210.0180.0110.0160.0530.0530.0530.0530.0530.0180.7090.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7170.7070.7220.7130.7241.0000.7130.7340.7090.7080.7070.7080.7250.7120.7070.7130.7200.1250.7070.0050.1110.7100.7070.0130.1660.0000.0510.0220.7080.7080.7071.000
IRFAMSVC0.7070.0190.0070.0060.0000.0020.0050.7070.0000.7480.1020.7070.7070.0200.0260.0080.0180.0480.0480.0480.0480.0480.0160.7100.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.9050.7070.7200.7320.7070.7131.0000.7310.7070.7070.7070.7090.7180.7070.7070.7100.7150.5740.7070.0130.1160.7070.7070.0100.1500.0050.0510.0090.7070.7070.7071.000
IRFSTAMP0.7070.0470.0140.0080.0040.0040.0030.7070.0060.9500.2830.7070.7070.0770.0480.0550.0550.0500.0500.0500.0500.0500.0740.7160.7070.7070.7080.7070.7080.7080.7070.7070.7070.7070.7070.7070.7070.7090.7070.7070.7460.7080.7820.7330.7090.7340.7311.0000.7080.7180.7090.7220.7740.7070.7090.7330.7630.2420.7070.0090.1430.7070.7070.0110.3820.0080.0510.0310.7070.7080.7071.000
IRHH65_20.7070.1860.0130.0040.0000.0000.0000.5780.1130.7080.0320.7070.7070.0540.0280.0370.0270.0540.0540.0540.0540.0540.0620.5820.7070.7100.7070.7080.7080.7070.7070.5770.7070.7070.7070.7070.7070.5780.7100.7070.5780.7100.5800.7070.8400.7090.7070.7081.0000.5990.7080.5950.7080.8320.5780.5820.7080.0360.7070.0000.1250.7780.5780.0060.0670.0000.0540.0900.5800.5790.7071.000
IRHHSIZ20.707-0.1540.049-0.150-0.067-0.0740.0590.5780.0780.7180.0720.7070.707-0.047-0.0030.040-0.025-0.059-0.059-0.059-0.059-0.059-0.0350.5290.7070.7090.7070.7080.7080.7070.7070.5780.7070.7070.7080.7070.7070.5780.7080.7070.5790.7080.1250.7090.7180.7080.7070.7180.5991.0000.7080.7070.7180.7410.578-0.2880.714-0.0530.7070.2620.3210.7290.5780.0040.1370.105-0.081-0.2920.5790.5780.707-0.006
IRINSUR40.7070.0150.0500.0040.0040.0030.0000.7070.0890.7090.2990.7070.7070.9830.0690.6760.6170.4180.4180.4180.4180.4180.8470.7130.7070.7080.7070.7070.7070.7070.7070.7070.7070.7080.7070.7070.7070.9280.7070.7070.7070.7090.7220.7070.7080.7070.7070.7090.7080.7081.0000.7070.7190.7111.0000.7150.7690.0310.7070.0140.1180.7140.7070.0090.1490.0000.0510.1020.7070.7070.7071.000
IRKI17_20.7070.1080.0000.0030.0060.0010.0040.5770.0490.7200.0880.7070.7070.0260.0170.0130.0290.0570.0570.0570.0570.0570.0330.5570.7070.7070.7070.7070.7070.7070.7070.5780.7070.7070.7070.7070.7070.5780.7080.7070.5810.7080.5040.7120.7200.7080.7090.7220.5950.7070.7071.0000.7250.7330.5780.5240.7130.0850.7070.0000.5140.7260.5770.0040.1350.0000.0530.3890.5780.5770.7071.000
IRMCDCHP0.7070.0690.0120.0180.0000.0000.0100.7070.0810.7760.4100.7070.7070.1830.0780.1190.1150.0900.0900.0900.0900.0900.2130.7310.7070.7080.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7230.7070.7070.7260.7090.7590.7190.7080.7250.7180.7740.7080.7180.7190.7251.0000.7070.7230.7470.8190.1670.7070.0140.1110.7100.7070.0160.3180.0120.0510.1640.7070.7070.7071.000
IRMEDICR0.7070.2310.0200.0120.0000.0000.0000.7080.1440.7070.0360.7080.7080.1030.0460.0720.0650.0630.0630.0630.0630.0630.1190.7160.7070.7080.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7120.7110.7070.7070.7110.7110.7070.8090.7120.7070.7070.8320.7410.7110.7330.7071.0000.7120.7180.7080.0310.7070.0030.1480.8760.7080.0060.0640.0060.0570.1250.7130.7120.7071.000
IROTHHLT0.7080.0180.0600.0060.0070.0050.0000.5770.1040.7090.3490.7070.7070.9830.0620.6770.5040.4180.4180.4180.4180.4180.9860.5830.7070.7080.7070.7070.7070.7070.7070.5770.7070.7080.7070.7070.7070.8130.7070.7070.5770.7090.5910.7070.7080.7070.7070.7090.5780.5781.0000.5780.7230.7121.0000.5850.7900.0310.7080.0130.0960.7770.5780.0080.1270.0000.0560.1170.5770.5770.7071.000
IRPINC30.7070.342-0.0730.313-0.012-0.024-0.0360.5790.1300.734-0.2970.7070.7070.0700.040-0.0430.0600.0600.0600.0600.0600.0600.0650.5750.7070.7100.7080.7080.7080.7070.7070.5780.7070.7080.7070.7080.7070.5860.7080.7080.5820.7080.3660.7110.7130.7130.7100.7330.582-0.2880.7150.5240.7470.7180.5851.0000.7490.1140.707-0.1130.1530.7140.579-0.0050.314-0.3100.0680.4880.5780.5780.707-0.008
IRPRVHLT0.7080.0340.0500.0220.0040.0000.0000.7080.2050.7640.6990.7070.7070.4210.0360.2970.2650.1840.1840.1840.1840.1840.4920.7140.7070.7090.7070.7070.7080.7070.7070.7070.7070.7080.7070.7070.7070.7900.7070.7070.7200.7130.7850.7150.7100.7200.7150.7630.7080.7140.7690.7130.8190.7080.7900.7491.0000.1360.7080.0190.1120.7230.7080.0090.3590.0080.0510.0310.7080.7080.7071.000
IRWELMOS0.0300.058-0.0080.220-0.040-0.029-0.0210.0330.0030.303-0.1820.0300.0300.0090.0320.0120.0120.0010.0010.0010.0010.0010.0040.0840.0290.0290.0320.0310.0320.0320.0290.0300.0300.0300.0310.0300.0310.0310.0290.0290.6970.0340.2200.4930.0400.1250.5740.2420.036-0.0530.0310.0850.1670.0310.0310.1140.1361.0000.030-0.0030.0580.0300.0350.0010.183-0.185-0.0010.0100.0330.0340.030-0.004
MAIIN1020.9930.0150.0000.0000.0000.0000.0000.7140.0000.7070.0360.7070.7070.0210.0190.0000.0170.0460.0460.0460.0460.0460.0420.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7080.7070.7070.7070.7070.7080.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7080.7070.7080.0301.0000.0000.1090.7080.7160.0130.0620.0000.0510.0090.7070.7070.7071.000
MEDICARE0.000-0.2620.1360.0110.162-0.0960.1230.0090.0390.010-0.1040.0000.000-0.127-0.0970.064-0.121-0.110-0.110-0.110-0.110-0.110-0.1120.0150.3450.0770.2010.1880.1870.2140.2140.0860.1780.3470.1030.3260.7070.3470.0870.2820.1860.0000.0780.0000.0000.0050.0130.0090.0000.2620.0140.0000.0140.0030.013-0.1130.019-0.0030.0001.0000.1390.0000.0060.0020.013-0.028-0.053-0.1190.0790.0800.000-0.007
NRCH17_20.1090.0260.1380.1060.2160.2720.1780.0900.0990.1370.0290.1090.1090.0680.0240.0500.0360.0620.0620.0620.0620.0620.0590.1980.2070.1330.1620.1560.1500.1760.1830.2770.5060.1460.4140.1470.1760.1240.1380.1530.1380.1110.0820.1200.1450.1110.1160.1430.1250.3210.1180.5140.1110.1480.0960.1530.1120.0580.1090.1391.0000.1360.0900.0000.0660.1080.0540.2890.1390.1380.1090.154
OTHINS0.7080.1730.0070.0070.0020.0000.0000.7080.0760.7070.1500.7080.7080.1390.0290.0950.0880.0740.0740.0740.0740.0740.1020.7150.7070.7080.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7110.7090.7070.7070.7850.7100.7070.7670.7100.7070.7070.7780.7290.7140.7260.7100.8760.7770.7140.7230.0300.7080.0000.1361.0000.7080.0080.0610.0000.0510.1210.7100.7100.7071.000
PDEN100.7160.0660.0160.0150.0000.0000.0070.8450.0340.7070.0250.7070.7070.0220.0170.0090.0180.0530.0530.0530.0530.0530.0310.5770.7070.7080.7070.7070.7070.7070.7070.5770.7070.7070.7070.7070.7070.5780.7070.7070.5770.7080.5800.7070.7080.7070.7070.7070.5780.5780.7070.5770.7070.7080.5780.5790.7080.0350.7160.0060.0900.7081.0000.0100.0590.0020.0530.0220.5780.5770.7071.000
PERID0.014-0.003-0.004-0.0140.0010.000-0.0060.0110.0000.0140.0030.0110.0110.0030.005-0.0010.0010.0090.0090.0090.0090.0090.0020.0080.0110.0100.0090.0090.0090.0080.0070.0080.0070.0130.0080.0170.0100.0100.0070.0080.0090.007-0.0070.0110.0080.0130.0100.0110.0060.0040.0090.0040.0160.0060.008-0.0050.0090.0010.0130.0020.0000.0080.0101.0000.0000.004-0.010-0.0020.0040.0070.009-0.003
POVERTY30.0620.0580.0330.0170.0080.0070.0060.0580.2010.3760.3540.0530.0530.1930.0430.1390.0890.1230.1230.1230.1230.1230.1980.1090.0540.0680.0580.0550.0590.0570.0540.0480.0530.0580.0570.0560.0550.1250.0550.0560.1540.0640.6140.1560.0750.1660.1500.3820.0670.1370.1490.1350.3180.0640.1270.3140.3590.1830.0620.0130.0660.0610.0590.0001.0000.0160.0000.0540.0510.0520.0540.076
PRVHLTIN0.000-0.0780.449-0.5390.1160.080-0.1050.0070.1030.0080.9460.0000.000-0.445-0.0900.312-0.402-0.237-0.237-0.237-0.237-0.237-0.4830.0220.3230.0840.2010.1810.1910.2150.2150.0700.1410.4540.0840.2790.2860.4540.0870.7070.1880.000-0.4710.0000.0000.0000.0050.0080.0000.1050.0000.0000.0120.0060.000-0.3100.008-0.1850.000-0.0280.1080.0000.0020.0040.0161.000-0.035-0.0290.0670.0690.0000.005
PRXRETRY0.0510.036-0.048-0.0290.014-0.014-0.0360.0550.0000.051-0.0170.0510.0510.0400.046-0.0370.0430.0900.0900.0900.0900.0900.0220.0510.0680.1040.1150.1230.1100.1060.0910.0540.0510.1030.0510.0940.0800.1030.0520.0980.1220.051-0.0230.0510.0510.0510.0510.0510.054-0.0810.0510.0530.0510.0570.0560.0680.051-0.0010.051-0.0530.0540.0510.053-0.0100.000-0.0351.000-0.0570.0510.0510.0510.005
PRXYDATA0.0090.2970.0980.1610.1000.024-0.0180.0160.0380.030-0.0110.0080.008-0.098-0.0430.049-0.091-0.033-0.033-0.033-0.033-0.033-0.1120.7620.0130.0780.0270.0370.0390.0270.0230.0180.0210.0250.0170.0180.0210.1170.0600.0220.0320.019-0.1020.0190.0560.0220.0090.0310.090-0.2920.1020.3890.1640.1250.1170.4880.0310.0100.009-0.1190.2890.1210.022-0.0020.054-0.029-0.0571.0000.0410.0170.0090.002
TOOLONG0.7070.0440.0770.0640.1400.1700.1110.5770.0230.7080.0340.7070.7070.0310.0140.0230.0170.0600.0600.0600.0600.0600.0260.5780.7150.7080.7100.7090.7100.7120.7120.5800.7310.7090.7130.7100.7110.5790.7090.7100.5800.7070.5790.7070.7100.7080.7070.7070.5800.5790.7070.5780.7070.7130.5770.5780.7080.0330.7070.0790.1390.7100.5780.0040.0510.0670.0510.0411.0000.8210.7071.000
TROUBUND0.7070.0260.0780.0650.1400.1700.1110.5770.0240.7080.0380.7070.7070.0230.0140.0180.0130.0570.0570.0570.0570.0570.0200.5770.7150.7080.7100.7090.7100.7120.7120.5800.7310.7090.7130.7100.7120.5790.7080.7100.5800.7070.5780.7070.7090.7080.7070.7080.5790.5780.7070.5770.7070.7120.5770.5780.7080.0340.7070.0800.1380.7100.5770.0070.0520.0690.0510.0170.8211.0000.7071.000
VEREP0.7070.0000.0000.0000.0030.0000.0000.7070.0050.7070.0030.7070.7070.0190.0170.0000.0160.0460.0460.0460.0460.0460.0160.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.7070.0300.7070.0000.1090.7070.7070.0090.0540.0000.0510.0090.7070.7071.0001.000
VESTR1.000-0.013-0.000-0.0100.0020.0040.0071.0000.0001.0000.0051.0001.0000.0010.0060.0030.004-0.003-0.003-0.003-0.003-0.0030.0041.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000-0.0161.0001.0001.0001.0001.0001.000-0.0061.0001.0001.0001.0001.000-0.0081.000-0.0041.000-0.0070.1541.0001.000-0.0030.0760.0050.0050.0021.0001.0001.0001.000

Missing values

2026-02-16T05:56:04.600653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-16T05:56:06.340748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PERIDIFATHERNRCH17_2IRHHSIZ2IIHHSIZ2IRKI17_2IIKI17_2IRHH65_2IIHH65_2PRXRETRYPRXYDATAMEDICARECAIDCHIPCHAMPUSPRVHLTINGRPHLTINHLTINNOSHLCNOTYRHLCNOTMOHLCLASTHLLOSRSNHLNVCOSTHLNVOFFRHLNVREFHLNVNEEDHLNVSORIRMCDCHPIIMCDCHPIRMEDICRIIMEDICRIRCHMPUSIICHMPUSIRPRVHLTIIPRVHLTIROTHHLTIIOTHHLTHLCALLFGHLCALL99ANYHLTI2IRINSUR4IIINSUR4OTHINSCELLNOTCLCELLWRKNGIRFAMSOCIIFAMSOCIRFAMSSIIIFAMSSIIRFSTAMPIIFSTAMPIRFAMPMTIIFAMPMTIRFAMSVCIIFAMSVCIRWELMOSIIWELMOSIRPINC3IRFAMIN3IIPINC3IIFAMIN3GOVTPROGPOVERTY3TOOLONGTROUBUNDPDEN10COUTYP2MAIIN102AIIND102ANALWT_CVESTRVEREPCriminal
025095143424131119999212299992999999999999999911212121999989811121121211121219991411121211223884.806004002610
11300514341312111999922211992999999999999999921212111999989811121121211121219991111112223221627.108114001521
267415143412121119999212299992999999999999999911212121999989811122111211121219992211112223224344.957984002410
3709251434021111199992221199299999999999999992121211199998981112112121212121999771123221122792.521934002710
4752351431061411199121229999299999999999999991121212199998981112212121112111111211112222221518.118534000120
547745143402111119999222299299991199999999992121212121989822122121212121219992311222223229129.229124003520
63314514343614111999921229999299999999999999991121212199998981112212121111121111111112222226561.895504004320
76376514342413111999922229929999599166662121212121989822121121211121219991111112222223341.718874000620
857796143413121119999222299299992299999999992121212121989822122121212121219991113212221223384.147894002120
96641614340111121999912211992999999999999999921112111999989811111111212121219996611232211222636.943984000610
PERIDIFATHERNRCH17_2IRHHSIZ2IIHHSIZ2IRKI17_2IIKI17_2IRHH65_2IIHH65_2PRXRETRYPRXYDATAMEDICARECAIDCHIPCHAMPUSPRVHLTINGRPHLTINHLTINNOSHLCNOTYRHLCNOTMOHLCLASTHLLOSRSNHLNVCOSTHLNVOFFRHLNVREFHLNVNEEDHLNVSORIRMCDCHPIIMCDCHPIRMEDICRIIMEDICRIRCHMPUSIICHMPUSIRPRVHLTIIPRVHLTIROTHHLTIIOTHHLTHLCALLFGHLCALL99ANYHLTI2IRINSUR4IIINSUR4OTHINSCELLNOTCLCELLWRKNGIRFAMSOCIIFAMSOCIRFAMSSIIIFAMSSIIRFSTAMPIIFSTAMPIRFAMPMTIIFAMPMTIRFAMSVCIIFAMSVCIRWELMOSIIWELMOSIRPINC3IRFAMIN3IIPINC3IIFAMIN3GOVTPROGPOVERTY3TOOLONGTROUBUNDPDEN10COUTYP2MAIIN102AIIND102ANALWT_CVESTRVEREPCriminal
4570899193679106141119999212299992999999999999999911212121999989811122121212121219991311212223221966.301614000110
4570923313679203131119912121199299999999999999991121211199998981112212121112121999231112222322516.659184002311
457109042367941212111999922211992999999999999999921212111999989811121111111121219992211122211227845.356254004921
457118814367910312111991222299299993299999999992121212121989822122121212121219991411222211224307.472844002020
4571275253679424131119999222119914999999999999992121211199998981112212121212121999271123222222682.483084004810
4571329663679413121119999222299299995996661621212121219898221221212121212199944112222222211212.106964001110
457144307367940311121299122299992999999999999999921112121999989811111111212121219992713232211225733.239984003520
457155217367940614121991222299299992699999999992121212121989822121111212121111212613122211221490.027424004010
45716292836794011111199992222991299999999999999992121212111989811112121212121219994411232222223847.137424005010
4571787483679404111119999222119929999999999999999212121119999898111211212121212199967112322112211357.793724001820